The Resource An Introduction to Markov State Models and Their Application to Long Timescale Molecular Simulation, edited by Gregory R. Bowman, Vijay S. Pande, Frank Noé, (electronic resource)

An Introduction to Markov State Models and Their Application to Long Timescale Molecular Simulation, edited by Gregory R. Bowman, Vijay S. Pande, Frank Noé, (electronic resource)

Label
An Introduction to Markov State Models and Their Application to Long Timescale Molecular Simulation
Title
An Introduction to Markov State Models and Their Application to Long Timescale Molecular Simulation
Statement of responsibility
edited by Gregory R. Bowman, Vijay S. Pande, Frank Noé
Contributor
Editor
Editor
Subject
Language
  • eng
  • eng
Summary
The aim of this book volume is to explain the importance of Markov state models to molecular simulation, how they work, and how they can be applied to a range of problems. The Markov state model (MSM) approach aims to address two key challenges of molecular simulation: 1) How to reach long timescales using short simulations of detailed molecular models 2) How to systematically gain insight from the resulting sea of data MSMs do this by providing a compact representation of the vast conformational space available to biomolecules by decomposing it into states—sets of rapidly interconverting conformations—and the rates of transitioning between states. This kinetic definition allows one to easily vary the temporal and spatial resolution of an MSM from high-resolution models capable of quantitative agreement with (or prediction of) experiment to low-resolution models that facilitate understanding. Additionally, MSMs facilitate the calculation of quantities that are difficult to obtain from more direct MD analyses, such as the ensemble of transition pathways. This book introduces the mathematical foundations of Markov models, how they can be used to analyze simulations and drive efficient simulations, and some of the insights these models have yielded in a variety of applications of molecular simulation
Member of
Dewey number
611.01816
http://bibfra.me/vocab/relation/httpidlocgovvocabularyrelatorsedt
  • 2Hime3xdQUE
  • mJ_Npgz4w04
  • wauvRzlnxQ0
Image bit depth
0
Language note
English
LC call number
QH506
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorName
  • Bowman, Gregory R.
  • Pande, Vijay S.
  • Noé, Frank.
Series statement
Advances in Experimental Medicine and Biology,
Series volume
797
http://library.link/vocab/subjectName
  • Medicine
  • Biology
  • Chemistry, Physical organic
  • Mathematics
  • Molecular Medicine
  • Theoretical, Mathematical and Computational Physics
  • Computer Appl. in Life Sciences
  • Physical Chemistry
  • Mathematics, general
Label
An Introduction to Markov State Models and Their Application to Long Timescale Molecular Simulation, edited by Gregory R. Bowman, Vijay S. Pande, Frank Noé, (electronic resource)
Instantiates
Publication
Note
Bibliographic Level Mode of Issuance: Monograph
Antecedent source
mixed
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
An overview and practical guide to building Markov state models -- Markov model theory -- Estimation and Validation of Markov models -- Uncertainty estimation -- Analysis of Markov models -- Transition Path Theory -- Understanding Protein Folding using Markov state models -- Understanding Molecular Recognition by Kinetic Network Models Constructed from Molecular Dynamics Simulations -- Markov State and Diffusive Stochastic Models in Electron Spin Resonance -- Software for building Markov state models
Dimensions
unknown
Edition
1st ed. 2014.
Extent
1 online resource (XII, 139 p. 65 illus., 48 illus. in color.)
File format
multiple file formats
Form of item
online
Isbn
9789400776067
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other control number
10.1007/978-94-007-7606-7
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
  • (CKB)3710000000078948
  • (DE-He213)978-94-007-7606-7
  • (SSID)ssj0001086337
  • (PQKBManifestationID)11613197
  • (PQKBTitleCode)TC0001086337
  • (PQKBWorkID)11054931
  • (PQKB)10545811
  • (MiAaPQ)EBC3092082
  • (EXLCZ)993710000000078948
Label
An Introduction to Markov State Models and Their Application to Long Timescale Molecular Simulation, edited by Gregory R. Bowman, Vijay S. Pande, Frank Noé, (electronic resource)
Publication
Note
Bibliographic Level Mode of Issuance: Monograph
Antecedent source
mixed
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
An overview and practical guide to building Markov state models -- Markov model theory -- Estimation and Validation of Markov models -- Uncertainty estimation -- Analysis of Markov models -- Transition Path Theory -- Understanding Protein Folding using Markov state models -- Understanding Molecular Recognition by Kinetic Network Models Constructed from Molecular Dynamics Simulations -- Markov State and Diffusive Stochastic Models in Electron Spin Resonance -- Software for building Markov state models
Dimensions
unknown
Edition
1st ed. 2014.
Extent
1 online resource (XII, 139 p. 65 illus., 48 illus. in color.)
File format
multiple file formats
Form of item
online
Isbn
9789400776067
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other control number
10.1007/978-94-007-7606-7
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
  • (CKB)3710000000078948
  • (DE-He213)978-94-007-7606-7
  • (SSID)ssj0001086337
  • (PQKBManifestationID)11613197
  • (PQKBTitleCode)TC0001086337
  • (PQKBWorkID)11054931
  • (PQKB)10545811
  • (MiAaPQ)EBC3092082
  • (EXLCZ)993710000000078948

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